An open source framework for advanced multi-physics and multiscale modelling of solid oxide fuel cells

Solid oxide fuel cells are high-efficiency renewable energy devices and considered one of the most promising net-zero carbon energy technologies. Numerical modelling is a powerful tool for the virtual design and optimisation of the next-generation solid oxide fuel cells but needs to tackle issues for incorporating the multi-scale character of the cell and further improving the accuracy and computational efficiency. While most of solid oxide fuel cell models were developed based on closed source platforms which limit the freedom of customisation in numerical discretization schemes and community participation. Here, an open source multi-physics and multiscale platform for advanced SOFC simulations consisting of both cell- and pore-scale performance models was developed using OpenFOAM. The modelling aspects are elucidated in detail, involving the coupling of various physical equations and the implementation of the pore-scale electrode in the performance model. The entire platform was carefully validated against experimental data and the other numerical models which were implemented in commercial software ANSYS Fluent and based on the lattice Boltzmann method. The cell-scale model is subsequently employed to study the effects of different fuels, i.e., pure hydrogen and different ratios of pre-reformed methane gas under various operating temperatures. It is found that the cell-scale model reasonably predicts the effects of these parameters on the cell performance, aligning well with the Fluent model. This study further identified the size of representative element volume with respect to the current density for the anode via the pore-scale model where the realistic microstructures reconstructed by a Xe plasma focused ion beam–scanning electron microscopy are employed as computational domains. It is found that a volume element size of 1243 voxels is sufficient to yield the representative current density of the whole. All these numerical investigations show that OpenFOAM is a potential multi-physics and multi-scale computational platform that is capable of accurately predicting both cell-scale and pore-scale performance and spatial information of solid oxide fuel cells. The developed models are also made public in GitHub to inspire community to further develop around it

π learning: a performance‐Informed framework for microstructural electrode design

Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physics-informed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries